Musical Improvisation and the Brain:

A Cross-Cultural EEG Study of Jazz and

Hindustani Musicians

Honors Thesis Submitted by

Marion Wellington

in partial fulfilment of the

Sc.B. in Music Cognition

Brown University

April 15th, 2016

Prepared under the Direction of

Dr. Michael Worden, Advisor

Dr. Monica Linden, Reader

Dr. Dana Gooley, Reader

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Table of Contents Acknowledgments........................................................................................................................... 4

Abstract ........................................................................................................................................... 5

1: Introduction ................................................................................................................................. 6

1.1: Review of Improvisation in Music Cognition ...................................................................... 6

1.1a: Role of Improvisation in Music Therapy ...................................................................... 15

1.2: Lack of Cross-Cultural Studies in Music Cognition and Real-World Applications in Music Therapy...................................................................................................................................... 17

1.3: Discussion of Jazz and Hindustani Musicology ................................................................ 19

1.3a: Jazz theory .................................................................................................................... 19

1.3b: Hindustani theory ......................................................................................................... 20

1.3c: Comparison of jazz and Hindustani improvisation ...................................................... 23

1.3d Comparison of the pieces: On the Sunny Side of the Street and the Raga Yaman ....... 28

1.4: Objectives ........................................................................................................................... 29

1.5: Hypotheses ......................................................................................................................... 29

2: Methods .................................................................................................................................... 31

2.1: Participants ......................................................................................................................... 31

2.2: Materials ............................................................................................................................. 33

2.3: Protocol .............................................................................................................................. 34

3: Results ....................................................................................................................................... 36

3.1: Preparation and selection of the data ................................................................................. 36

3.2: Descriptive Analysis .......................................................................................................... 38

3.2a: Spectrograms ................................................................................................................ 38

3.2b: Averaged Alpha ............................................................................................................ 40

4: Discussion ................................................................................................................................. 42

4.1: Discussion of the results..................................................................................................... 42

4.2: Limitations of this study..................................................................................................... 46

4.3: Suggestions for future studies ............................................................................................ 49

5: Conclusion ................................................................................................................................ 51

6: References ................................................................................................................................. 52

7: Appendix A: Musical Terms ..................................................................................................... 57

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Western...................................................................................................................................... 57

Indian ......................................................................................................................................... 61

Other .......................................................................................................................................... 63

8: Appendix B: see folder ............................................................................................................. 64

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Acknowledgments This study was only made possible by an enormous collective of people, for whom I’m very grateful. To Michael Worden, for accepting the half-baked study I brought forth and helping me make it into something of which I’m proud. To Monica Linden, for facilitating my interest in this field since day one and helping me navigate my... singular undergraduate career since. To Dana Gooley, for helping me bridge the gap between science and humanities with curiosity and enthusiasm. To Carlos Aizenman, for taking on my Independent Concentration and giving me as many resources as you could muster. To Chuck Royce, for not only giving undergraduates the opportunity to delve into their passions and create unorthodox projects, but also for giving us the freedom to fail. To Kerrissa Heffernan, for telling me I’m “weird, and that’s a good thing.” To David Sheinberg, for lending me your EEG system on our first meeting. To The Moore Lab, for letting me pipette electrode gel onto the head of one of your lab members. To Stephanie Jones, for giving me both advice and reassurance. To Peggy Chang, for asking critical questions while giving absolute support. To Peter Bussigel, for lending me technical equipment and giving me life equipment. To Tom, for encouraging me to go for it. To Shantala Hegde at the National Institute of Mental Health and Neuro Sciences and Stephen McAdams at Music Perception and Cognition Lab at McGill University, for responding to my out-the-blue emails not just with a yes, but with continuous warmth and guidance throughout my summer and beyond. To Deepak Ullal, for attempting to fix the EEG headset not once, but twice, and sharing your joy and skill with the santoor. To Eric Lewis, for hashing out experimental design with me and showing me a prime example of Montreal’s free jazz. To Hélène Martel, for supplying me with participants in an unknown city. To all of my participants, for bringing patience and excitement to each trial. To Colette, Mahoro, Robert, Kalie, Dhanya, Nigesh, Shreena, Anushka, Neil, and Ria, for helping me add new meaning to my definition of home. To Bryn and Jamie, for not only making the GISP that started it all, but also inspiring me to further myself as a scientist and a musician. To Morgan, for being my music cognition rock in a campus frustratingly fixed on the visual system. To my friends, for encouraging me to do what makes me happy and reminding me to take care of myself. Finally, to my parents, for pushing me to do well, pushing me to do good, and supporting me in whatever that means to me. I love you. Thank you all so much for helping me get to the place I am today. I am overwhelmed with gratitude and appreciation.

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Abstract Musical improvisation is a highly complex creative process for which there is growing

interest in the field of music cognition. It has been studied from a variety of perspectives, from

classical string players in front of an audience to jazz pianists trading solos to freestyle rap

artists. These studies have pointed to an expertise-related difference in brain activity, supporting

Pressing’s model of practice-contingent automation of lower-level processes. However, the

current literature on improvisation is quite Western-centric, with no present studies of

improvisation outside the Western tonal system. I attempt to reconcile that gap by conducting an

electroencephalography (EEG) study of improvisation in jazz and Hindustani musicians to

examine the differences between performing composed music and improvising and to compare

these differences across cultures. I focused on the alpha band frequencies (7.5-12.5Hz) in the

frontal and posterior regions, as high alpha power has been found to correspond with divergent

thinking tasks such as improvisation. Though the Hindustani participants had low alpha power

during the composed condition, they consistently had higher alpha power than the jazz

participants during the improvised condition, both in the frontal and posterior regions. Both of

these findings may be indicative of Hindustani music tradition, both in its melodic complexities

and demanding practice time; the Hindustani composed content may have been inherently more

difficult than the jazz content and overall the Hindustani participants practice more frequently

and at greater length than do the jazz participants. This study, though not comprehensive, acts as

a pilot study for future inquiry into this field of cross-cultural musical improvisation.

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1: Introduction Improvisation is the act of spontaneous musical composition. It is a form of artistic

creation that is physically and temporally restricted; it incorporates elements of composition and

performance together in real time. It follows that improvising requires extensive and expansive

cognitive function. As the fields of music cognition and the neuroscience of creativity are

becoming increasingly popular, the neuroscientific study of musical improvisation is also

garnering interest in the scientific community. However, existing studies have been quite

Western-centric. My thesis seeks to address this disparity by approaching the study of

improvisation from a cross-cultural lens, by analyzing electroencephalography (EEG) recordings

from both jazz and Hindustani musicians as they improvise and perform previously composed

music.

1.1: Review of Improvisation in Music Cognition

Because improvisation is a creative process, the way in which it is conducted in an

experimental setting can vary widely. Most of the current literature uses pianists, from either

classical or jazz backgrounds, and compares brain activity recorded with fMRI or EEG between

playing composed music and improvising with varying conditions. Researchers have asked a

variety of questions in their studies, but they all investigate the neural substrates of

improvisation. Table 1 shows an overview of the literature reviewed.

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Table 1: Abbreviated summ

ary of musical im

provisation literature review

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Though the reviewed studies highlight a diverse range of brain regions, there were key

regions that were consistently implicated, either by their significant activations or deactivations.

The pre‐supplementary motor area, which is implicated in the temporal control of motor

sequences, was found to be consistently activated during improvisation (Bengtsson et al., 2007;

Liu et al., 2012; de Manzano and Ullén, 2012; Pinho et al., 2014; Donnay et al., 2014). The left

inferior frontal gyrus, or IFG, is associated with retrieval of long term memory, especially in

relation to language; it too was active in multiple studies (Limb and Braun, 2008; Berkowitz and

Ansari, 2010; Liu et al., 2012; de Manzano and Ullén, 2012; Donnay et al., 2014). The IFG is

often activated in syntax retrieval in language, implying that musical improvisation could be

utilizing syntactical musical knowledge from long term memory to generate novel musical

sequences. The dorsal premotor cortex, which projects directly to the spinal cord and is involved

in volitional control of movement, also showed activation (Bengtsson et al,. 2007; Berkowitz and

Ansari, 2010; de Manzano and Ullén, 2012; Liu et al., 2012).

Two other regions, the dorsolateral prefrontal cortex, or DLPFC, and the medial

prefrontal cortex, or MPFC, are contested in their roles in improvisation. The DLPFC is the

endpoint of the dorsal pathway, which ‘tells’ other brain regions how to interact with stimuli. It

is a major site of executive function, working memory, active decision making, and cognitive

flexibility (Limb and Braun, 2008). In contrast, the MPFC is associated with spontaneous

thought processes, such as mind‐wandering (Limb and Braun, 2008). The DLPFC has been

characterized as a location of executive function that interacts with external attention and stimuli,

while the MPFC is thought to be a location of self‐generated stimulus‐independent cognition. In

some studies, there was activation in the MPFC and deactivation in the DLPFC, implying that

improvisation might be a largely internal, subconscious mechanism (Limb and Braun, 2008; Liu

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et al., 2012; Pinho et al., 2014). It supports the concept of suppression of executive control

systems and the activation of default mode regions in improvisation. As Limb and Braun state,

[the activation of the MPFC] may reflect a combination of psychological

processes required for spontaneous improvisation, in which internally motivated,

stimulus-independent behaviours unfold in the absence of central processes that

typically mediate self-monitoring and conscious volitional control of ongoing

performance (2008: 4-5)

In other studies, the DLPFC showed activation, while the MPFC did not (Bengtsson et al., 2007;

de Manzano and Ullén, 2012; Donnay et al., 2014). Those studies argued that sensory input

integration and selective retrieval are crucial to the act of improvisation.

So, how can these competing arguments be reconciled? The answer may be in line with

Jeff Pressing’s expertise-contingent model of improvisation (1988). Pressing postulates that

musical improvisation is an acquired skill through extensive training. Improvisation is

demanding of many mental faculties simultaneously, such as sensory encoding, motor control,

and memory retrieval. Pressing believes that practice automates such lower-level processes,

allowing the performer to focus their faculties on higher-level processes, such as musical idea

generation and evaluation.

In the studies in which the DLPFC was active, the musicians were either “trading fours”

(where each musician takes a solo every four measures) with another musician or they were

classically trained musicians asked to improvise. The former condition requires the integration of

sensory input from their fellow musician, and the latter involves musicians with expertise in a

related but not exactly similar field. The studies that involved activation in the MPFC (and

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deactivation of the DLPFC) used jazz musicians at a least a semi‐professional level. Classical

musicians are rarely asked to improvise, while improvisation is an integral element of jazz. So,

though the classical musicians had extensive musical expertise, their lower‐level processes may

not be as automated as the musicians that improvise regularly. Conversely, those with

improvisational expertise may have those processes more automated (as Pressing predicted) and

can thus allocate their faculties to higher‐level processes.

There are other ways in which expert improvisers differ from those without expertise.

Pinho and her colleagues studied participants with varying experience in both jazz and classical,

stating that

...greater functional connectivity of the frontal brain regions seen in the most

experienced participants may reflect a more efficient integration of

representations of musical structures at different levels of abstraction. A higher

functional connectivity of the seed regions was observed with premotor regions

and parietal and prefrontal association cortex, as well as with primary

sensorimotor cortex and the cerebellum, suggesting that the training-related

functional reorganizations may affect both cognitive and sensorimotor aspects of

improvisation. (Pinho et al., 2014: 6161)

In the studies that used EEG, all three suggested a more widespread connectivity in the

frontal and sensory regions, supporting Pinho’s postulation. This higher functional connectivity,

along with the activation of the MPFC and the deactivation of the DLPFC in expert improvisers,

all points to a top-down process in which attention is directed less at lower-level processes such

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as motor sequences and planning, but instead to more upper-level ideas such as overall form and

divergent thinking. Limb and Braun state that

It has also been suggested that deactivation of the lateral prefrontal regions

represents the primary physiologic change responsible for altered states of

consciousness such as hypnosis, meditation or even daydreaming. This is

interesting in that jazz improvisation, as well as many other types of creative

activity, have been proposed to take place in an analogously altered state of mind.

Moreover, a comparable dissociated pattern of activity in prefrontal regions has

been reported to occur during REM sleep, a provocative finding when one

considers that dreaming is exemplified by a sense of defocused attention, an

abundance of unplanned, irrational associations and apparent loss of volitional

control, features that may be associated with creative activity during wakefulness

as well. (2008: 5)

Alpha waves are oscillations in the frequency band of 7-13Hz that are usually associated

with a relaxed state of mind, similar to what is described above. Indeed, other EEG studies of

creativity (though not specifically improvisation) have pointed to alpha waves as being widely

prevalent during creative processes. A robust review on the neuroimaging of creativity done by

Arden et al. (2010) argues that the current literature is too disparate to make any general

conclusions. However, it did state that studies that used the Alternate Uses Test (or a variant on

such) all showed high levels of alpha synchronization. The AUT is a test done to measure

creative ideation; the classic example is to give the subject a common object (e.g. a brick) and

ask them to list as many possible uses for the object as they can think of (e.g. as a building for a

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mini King Kong to topple in a reenactment). It demonstrates divergent thinking, which is the

generation of many possible solutions to an open situation. As Fink and Benedek state, “It

[divergent thinking] is conceptualized as a cognitive process involving both the retrieval of

existing knowledge from memory and the combination of various aspects of existing knowledge

into novel ideas” (2012). Musical improvisation can easily be classified as a type of divergent

thinking; as a solo progresses, the musician generates multiple concepts of how the phrase can

start, follow through, and end, given the musical framework in which they are operating.

Fink and Benedek (2012) addressed Arden et al. (2010) in their own review of creativity

literature. They argued that their summary was inconclusive because they were not focusing on

any particular aspect of creativity. Fink and Benedek reviewed literature that related specifically

to divergent thinking. Consequently, they found an overall trend of more alpha power in

divergent tasks than rest or convergent tasks. The more creative the task, the higher the alpha

levels present. Fink and Benedek also found that frontal alpha synchronization correlates with

top-down processing, a trait of creative processes such as musical improvisation. To clarify the

relationship of alpha waves to divergent thinking, Jauk et al. recorded EEG of participants who

did convergent and divergent tasks, and found that while convergent thinking resulted in a strong

task-related desynchronization of alpha activity, divergent thinking resulted in synchronization

of alpha activity, especially in frontal cortical sites (2012).

The prevalence of alpha synchronization (a frequency band most associated with

relaxation) and the deactivation of lateral prefrontal regions, specifically the DLPFC (which has

been associated with daydreaming, defocused attention, and other altered states of consciousness

by Limb and Braun, 2008) point to the presence of “flow” in improvisation and other creative

processes. Csikszentmihalyi defines flow as “an almost automatic, effortless, yet highly focused

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state of consciousness” (1996) in which a task is performed to the best of an individual’s ability.

Though the term stems from positive psychology and is referenced in a variety of fields, many

neuroscientists have become intrigued with the phenomenon, its neural underpinnings, and how

it relates to creativity. Flow has been cited to occur in many different situations, such as sports or

videogames, but it is most researched as it relates to creative processes, such as writing, theater,

and improvisation, because it is thought that flow and creativity are inextricably linked. Dietrich

identifies free-jazz improvisation as one of the more common situations in which someone can

experience flow:

In free-jazz improvisation, the musician arranges units into a flowing string.

Because the string progresses by each unit triggering the next, the application

becomes part of the procedure. The overall product can be novel (indeed, if the

string is long enough it must be novel due to the complexity of the musical

system). The full string can even be multi-dimensional, but each individual step is

not. It is the number of distinct reflexive loops as well as their level of

automatization that determine the quality of the flow experience. It should be

noted that such increased implicit expertise does not necessarily lead to the skills

representation in the explicit system. (2004: 756)

Dietrich believes that flow comes out of the interplay between explicit and implicit

learning, and that “optimal performance involving a real-time sensory-motor integration task is

associated with maximal implicitness of the task’s execution. Given that the explicit system is

subserved by prefrontal regions, it follows from this proposal that a flow experience must occur

during a state of transient hypofrontality that can bring about the inhibition of the explicit

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system” (2004). This concept of hypofrontality intersects with the evidence of expertise-related

deactivation of the DLPFC, and the corresponding frontal alpha synchronizations.

Though many music cognitivists conduct pure research for the sake of understanding

music in the brain, the field can also be used in a direct application to health: music therapy.

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1.1a: Role of Improvisation in Music Therapy Music therapy is a burgeoning field in health care, one that is beginning to gain more

credibility as a substantive way to assist medical treatment. Music therapy has been successful in

a variety of health issues, from alleviating mental illness to physical and neuro-rehabilitation in

stroke victims. While there are many forms of music therapy (listening, playing, and creating

music to name a few), improvisation is being used increasingly in a variety of treatments to

engage its complex cognitive properties. In a robust review of the current literature regarding

improvisation and health, MacDonald and Wilson note “Improvisation in music therapy is seen

to have specific benefits for particular populations including the amelioration of neurological

damage, improvements in mental health conditions, reductions in stress and anxiety, and

improved communication and joint attention behaviours in children with autistic spectrum

disorders” (2014). They attribute the therapeutic effects to multiple aspects of the practice. The

mental faculties that improvisation demands, whether or not one is a seasoned musician, are

considerably large. This puts the patient in a state of focus through a creative process, in a way

allowing them a reprieve from otherwise taxing states of mind. Second, improvisation creates a

venue for the patient to express their emotions in a non-verbal, non-explicit way; patients who

have difficulties articulating themselves can release those often repressed feelings in a productive

manner (Wigram, 2004; Smeijsters and van den Hurk, 1999; O’Callaghan, 2004; Volkman,

1993). Lastly, the non-verbal social interaction that paired or group improvisation provides has

shown significant improvements in populations who otherwise have trouble interacting, such as

patients on the autism spectrum (Geretsegger et al., 2012; Simpson and Keen, 2011).

While most studies of music therapy and its effect deal with behavioral results only,

others have recorded biological and neurological data to corroborate the behavioral findings. In

studies of patients with neurological damage, usually due to stroke, scientists have found

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remarkable physical improvements in patients who improvise, usually due to the rhythmic

entrainment of the musical interaction (Aigen, 2009; Tomaino, 2013). Fachner (2012) recorded

EEG data of 62 patients with depression and comorbid anxiety. Half of the patients received

improvisational music therapy in addition to their standard care while the other half served as

controls (receiving standard care alone). By comparing recordings done at both the inception of

the treatment and three months in, they found that improvisational music therapy not only

significantly reduced depression and anxiety symptoms, but also elicited long-term absolute

alpha power increases in the left fronto-temporal lobe and theta power increases in the left

fronto-central and right temporoparietal lobes.

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1.2: Lack of Cross-Cultural Studies in Music Cognition and Real-World Applications in Music Therapy

Clearly, the inquiry into the improvisational brain is growing. However, all of these

studies (not unlike the rest of music cognition studies) are Western-centric; they focus on

improvisation purely in the context of the Western tonal system, whether it be jazz, classical, or

more recently, hip-hop. But there are many genres of music all around the world that have strong

themes of improvisation. Middle Eastern folk, Indian classical, Javanese Gamelan drumming,

Native American folk, West African Mande, and more incorporate improvisation into their

compositions and performances. So why has the focus been exclusively on Western jazz and

Western classical? One can argue that we have easier access to these participants, and keeping

the material studied constant allows for more congruence between studies. However, that narrow

parameter precludes the possibility of discovering whether the neural phenomena studied are

inherently human or just inherently Western.

There is a small body of work in music cognition that addresses this disparity by

comparing aspects of Western music to its non-Western counterparts. A study on musical phrase

processing in German subjects while listening to Western and Chinese excerpts showed that

there was a much stronger sensori-motor network engaged while listening to culturally familiar

music versus unfamiliar (Nan et al., 2008). This implies that our musical entrainment is not all

innate, but is partially due to the prevalence of the tonal system in our everyday lives. A study in

cross-cultural music comprehension in which Western musicians and nonmusicians listened to

Western and Chinese musical excerpts showed that although the neural correlates while listening

did not significantly differ across the two genres, the subjects’ recall performance was much

poorer for the non-familiar genre (Morrison et al., 2003). Lastly, a study was recently conducted

in which the emotional response of both Westerners and Pygmies were recorded as they were

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exposed to genres from each culture (Egermann et al., 2015). They showed that while the

perception of the music’s emotional value varied across populations, physiological arousal due to

low-level acoustic properties was relatively constant across cultures. This model of comparing a

specific component of music within a Western paradigm and a non-Western paradigm is quite

effective for discerning what is culturally derived and what is naturally innate. Unfortunately,

such a modest body of work fails to fully address the nature-nurture debate.

This investigation of improvisation cross-culturally is intended to add to this

conversation, as currently there exists no such study in the literature. The study of improvisation

from a cross-cultural lens is important because it helps remove a cultural bias from the results. If

there are commonalities between the two groups, this may imply innate abilities or cognitive

processes not molded by their environment. If there are dissimilar properties, hypotheses can be

drawn about how their respective cultures influence the data. Further, these findings could

potentially make improvisation a more accessible health tool to other cultures, possibly globally.

It has been shown that improvisation can be used as a powerful tool in music therapy and

health. However, as stated previously, the vast majority of these studies have been conducted in

the Western sphere with Western patients in mind. If the practices of improvisation between

Western and Indian cultures (however they may differ theoretically) have similar neural

substrates, we can suggest that improvisation can be utilized as a tool for health in India as well

as the West.

This study focuses on improvisation within two musical groups, Western jazz and Indian

classical Hindustani music. I have collected EEG data from vocalists in both traditions as they

perform previously composed music and improvise within their common musical parameters.

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1.3: Discussion of Jazz and Hindustani Musicology

Why choose jazz and Hindustani as the two forms of music to compare? While the genres

come from much different origins, they have elements of both composition and improvisation in

their performances. The following section examines the theory and practice of improvisation in

both genres, and how they may be compared. See Appendix A for a glossary of musical terms.

1.3a: Jazz theory Jazz is difficult to define, as it had evolved both linearly over more than a century and

laterally over a multitude of genres and subgenres. Its roots are in New Orleans in the early

1910s, when Black Americans combined ragtime, brass bands, slave rhythms and melodies,

blues, African rhythms, and more, using a polyphonic form of improvisation where musicians

would weave their sounds in and out of each other’s in a state of harmonious cacophony. Louis

Armstrong is credited with introducing solo improvisation to the greater jazz world, in which a

single musician would take the helm as the others would quiet into the background and would

then take turns. Over the course of the 20th century, different flavors of jazz took center stage,

from stride to big band to swing to bebop to hardbop to modal jazz to free jazz, with subgenres

spinning off and influencing other genres, such as Cu-bop, RnB, soul, funk, smooth jazz, and

other jazz fusions.

Though jazz has been through many epochs and styles, there are some overarching

themes that remain relevant to the larger genre. For the purposes of this paper, I will focus on the

melodic and harmonic content of swing, as that is the area of jazz used in this experiment. Swing

is characterized as a type of performance style, popularized by big bands in the 1930s, that has

an idiosyncratic bounce in which the eighth notes are performed such that the downbeats and

upbeats have ⅔ and ⅓ of the beat respectively. A good example of a tune that switches between

https://docs.google.com/a/brown.edu/document/d/1h21bz9GcU7dsJhUfnyJ5fcE018FPRJ3SZ8ZFe3gyjpo/edit?usp=sharing

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swing and “straight” playing is “Caravan” by Duke Ellington, in which the chorus starts with a

straight feel and then jumps into swing feel. The repertoire used in swing, and in many other

iterations of jazz, are called standards, having come from jazz composers, Broadway show tunes,

Hollywood musicals, and other sources. These standards (and other jazz tunes) have a melody

(called a head, or the chorus) and harmony (called a chord progression, or changes) that can be

altered by performers often to great lengths. A hallmark of jazz harmony is the use of seventh

chords, rather than triads, as the building blocks for a progression. Additionally, another pillar of

jazz harmony is the authentic cadence (V-I) preceded with a predominant ii chord, ii-V-I.

Tensions, unusual scales, and modes are all devices used in composition, reharmonization of

standards, and improvisation.

The chord changes are usually supplied continuously throughout the piece by a

polyphonic instrument, like a piano or guitar, with drums supporting the rhythm and the bass

supporting the harmony. A vocalist, a horn, or any other monophonic instrument usually

performs the head, though any other instrument (save the drums) can supply that. Every musician

in the combo, or big band, is able to improvise on the piece (including the drummer). A melodic

improviser needs to know the restraints of a tune: the meter, form, and harmony dictate the

directions the musician can go in their improvisation.

1.3b: Hindustani theory Hindustani music is as old as jazz is new, as static as jazz is dynamic. Hindustani music

in its pure form is relatively untouched by modern music styles and forms. This is evident in its

form, the overall structure of a piece. Jazz pieces follow the form found in Western classical and

pop music in which they have very distinct sections that are repeated later on. Hindustani music

follows a more linear form. There are distinct movements, but they are not usually repeated.

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Likewise, the improvisation/composition aspect is much less dichotomized in Hindustani music

than in jazz music, as will be discussed further on. These differences are most likely due to both

the geographic and temporal distances between the genres.

Hindustani music is passed down from guru to shishya without written notation. The

musical content of the Hindustani tradition is remarkably complex and requires decades of

training to master. Vocalists usually start in their early adolescence and will practice a few

selections for many hours every day. While Western and Bollywood music have influenced the

mainstream Indian music heavily, the classical genres have stayed relatively constant, reflecting

the values of tradition and spirituality that many of the musicians possess.

Unlike jazz and other Western musics, which have rhythm, melody, and harmony as their

main building blocks, Hindustani music does not regard harmony highly. Instead, the melody

and rhythm, the raga and the tala, are what drive the performance. In place of harmonic changes

is a drone, usually from an instrument called the tanpura, which resonates at the shadja and

pancham swaras of the sargam (the tonic and dominant notes of the scale in Western

terminology). The tala is usually driven by the tabla, a percussion instrument that can be

manipulated to have a bent pitch. A tala is a repeating, rhythmic phrase that cycles at a variety of

beats with specific subdivisions. It does not have a fixed tempo, and can speed up or slow down

at the will of the performers during a piece.

There are many instruments that can play the melody, such as the sitar and santoor, but

the voice is the primary melodic instrument of Hindustani music. The raga (also known as a

raag) is the melodic backbone of a performance. Its closest Western analogue is a mode, though

in Hindustani music the raga is more than a scale; it is a color, a feeling, a passion. As Ravi

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Shankar, the prolific sitar player attributed with popularizing Indian classical music in the West,

puts it:

A raga is a scientific, precise, subtle and aesthetic melodic form with its own

peculiar ascending and descending movement consisting of either a full seven

note octave, or a series of six or five notes (or a combination of any of these) in a

rising or falling structure called the Arohana and Avarohana. It is the subtle

difference in the order of notes, an omission of a dissonant note, an emphasis on a

particular note, the slide from one note to another, and the use of microtones

together with other subtleties that demarcate one raga from the other.

Every raga has unique pitch content and intricate pitch relationships; the performance

and exploration of a raga can be up to an hour long. There are thousands of ragas; most vocalists

learn the most common ragas and will choose a couple to work on for many years. It is not just

the pitch content, but the way in which certain notes are prioritized and approached that

comprises a raga. The vaadi and the samvaadi are the two most important notes, aside from the

home note, and the way they are approached can distinguish two ragas with the same pitch

content. The scales themselves, the sargam, are quite comparable to Western scales. The

shortened names of each of the basic seven swara are used in the same way solfege is used in

Western music: Sa Re Ga Ma Pa Dha Ni (Sa), and the natural scale is the same as the Western

Ionian, or major scale. The swara R, G, D, and N can be either natural or flat, the M can be

either natural or sharp, and S and P can only be natural, totaling in the same pitch content as the

Western chromatic scale. The sargam differs from a key in that it can be moved in pitch, until

the S is fixed.

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There are as many striking similarities between the two genres of music as there are

differences. Both systems have similar collections of pitches in which a piece will (usually)

remain. Both highly value rhythm and melody, especially valuing a singular instrument in the

foreground of the ensemble (though that instrument may change throughout a piece). Both,

obviously, value both the original composition, variations upon it, and improvisation within a

given structure. Their respective approaches to improvisation reflect these comparisons.

1.3c: Comparison of jazz and Hindustani improvisation Before engaging in a comparison of their improvisation styles, I want to acknowledge the

differing meanings of improvisation globally. Ethnomusicologist Nettl argues against the

Western dichotomy of improvisation and composition. In Western music the distinction between

the two forms of creation is usually made based on the presence or absence of written notation of

the piece. Nettl, in his comparative approach, explores the multitude of cultures that do not have

a written notation system and yet produce music that the artists themselves would not label as

improvised (Nettl 1974). For some, such as Persian music, the performance of the dastgah is not

measured in notes and durations, but in the essence of what the performer was trying to express.

Though a Western listener may hear each performance as two different improvised pieces, a

native listener will experience the same essence, and thus the same “composition.” Likewise, a

Hindustani performance of a raga has improvisational elements throughout, but it is the essence

of the raga and the way it is expressed that usually remains constant.

In jazz, however, composition and improvisation are much more dichotomized. The form

of a typical performance is based off a composition, usually a standard, that the big band or

combo will play as written. As stated previously, there is the head (the melody) and the changes

underneath it (the harmony). The group will go through the head, and then members of the group

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will usually take turns improvising over the changes on repeat. The number of solos played is

entirely up to the band, and can take anywhere from a minute to an hour. To end the piece, the

members of the group will signal the front musician to play the head again (usually this time

with more embellishments) and then the piece will end, perhaps with a coda. Though the form is

quite dichotomized, there is some interplay between the improvisational and compositional

moments of a performance. Each musician who plays a piece will interpret the composition in

their own way, adding variations and embellishments to the head (and/or the changes) as they see

fit. Likewise, many musicians draw melodic and rhythmic phrases from the head during their

improvisations, using it as a home-base of sorts. Before this experiment was conducted, each

participant was interviewed about their experience and relationship to their music and own

personal craft. One of the jazz participants stated in her interview that “the previously composed

melody is like a ground-base to lean on in improvisation.”

Though every jazz musician has their own method of improvisation, many develop and

practice an arsenal of phrases, scales, and licks that they can insert and incorporate into their

solo. Motivic improvisation is a technique commonly used by jazz cats in which the musician

will introduce a motive (a short musical idea) and then develop it, changing its length,

modulating up or down, inverting it, etc. etc. Though a solo is technically improvised, not all of

it is created exactly in that moment1. When asked about the thought process during

improvisation, one jazz participant stated:

1 For more in-depth insight into the thought process of a jazz improviser, read Thinking in Jazz: the Infinite Art of Improvisation by Paul Berliner (1994).

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my ears are really reliable, so I most times go with them, but there are patterns you

learn...you can dig through the patterns you already know and use them...but I prefer to

go by what I’m hearing. After that, once I’m comfortable with the chords and everything,

I try to build a little story around the improvisation...I try to scat as if you’re having a

conversation.

While jazz improvisers must follow the harmonic changes, Hindustani improvisers must

operate within the chosen raga. As stated before, the raga is the melodic form that is trying to

express a feeling or color. When asked about the thought process during improvisation, one

Hindustani participant said:

It’s all about the raag, you must focus on making the proper note...you give preference to

the note, then comes the raag, then comes the composition and everything...you immerse

in the raag, and everything else will come on its own.

A typical performance starts with the alap section, in which the performer, usually a

vocalist, will introduce and develop the raga slowly and deliberately. Then comes the jor, the

part in which the rhythm is established without the drum, and the lead musician will perform

many variations of the raga. The jor gives way to the gat, the fixed composition of the raga. The

tabla and subsequently the tala is introduced. After that, the musician can improvise freely

within the raga and tala, analogous but not the same as the changes and the time signature in

jazz. The musician can then return to the gat, speeding up and intensifying until it ends in the

jhala, a fiery and dazzling section (Shankar 2009). Though the Hindustani form is somewhat

reminiscent of the jazz form of theme → improvisation → theme, the lines between the

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composition and improvisation are much more blurred. The lead musician focuses on the

tensions of the raga and the overall energy of the piece as it unfolds, rather than following the

harmonic changes of a standard. For the purposes of this study, I asked the Hindustani

participants to perform in such a manner that contrasted the improvisation and composition

elements of a performance, so as to be comparable to the jazz performance.

Though the parameters of improvisation differ between the cultures, divergent thinking is

prevalent in both. When improvising, every note is a springboard from which a multitude of

ideas can leap. In jazz, the directions it can go are mainly dictated by the harmonic changes,

though modal improvisation can be and is widely used. In Hindustani, the options lie in the

swara of the raga, and the relationship of each note to the other. Regardless of the context, a key

element of improvisation is the rapid evaluation of multiple options and the rapid and continuous

decisions between them; divergent thinking at hyperspeed.

In both groups, I asked the participants about the concept of flow.2 Some participants

knew the concept, others understood once I explained it to them. Overall, the jazz participants

had mixed experiences about flow, while the Hindustani participants quite generally experienced

flow consistently. One jazz participant said this about flow:

I’ve had it multiple times, like where you go offstage and I really felt like something was

happening because of the reaction of the crowd, but I completely black out...it’s as if part

of your brain goes numb, almost like the state you’re in while you’re dreaming, you don’t

2 One item of import is for the majority of my participants, English was not their first language (some were French, one was Portuguese, and all of the Hindustani vocalists were Indian) and so there may have been some miscommunications. However, I believe in these excerpts the interaction was mutually understood.

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have any control of it...but it’s still in the chords, you won’t go out or anything, but it’s

not conscious…

[in order to experience flow] usually I have to be with people and musicians that I’m

really comfortable with, people that value the role of the singer...when you get musicians

who respect you and like what you do, they listen to you...usually at that time I feel like I

have the space to create something.

When I asked the Hindustani participants the same question about flow, one immediately

stated:

Yes, yes, always experiencing flow...it’s never preset, when we’re about to start the

performance, previous to that my mind is completely blank, I start fresh and go and build

on the raag… and every time it’s new….

[I experience flow] anytime where I sit to sing, at home or on the stage, my mind will go

blank… it [the raga] will start on its own.

Overall, it did seem as if the Hindustani vocalists were much more able to access flow

state, which as previously stated correlates highly with alpha. This could be explained by

Pressing’s expertise model; as explained in more detail in the Methods section, the Hindustani

vocalists on average have both been practicing their craft for longer and practice more on a

weekly basis. The interactions with flow during improvisation could largely be due to the

relative comfort with their art.

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1.3d Comparison of the pieces: On the Sunny Side of the Street and the Raga Yaman For the jazz section of the study, I chose a standard that is relatively well-known by jazz

musicians: On the Sunny Side of the Street. Written in 1930 for the Broadway show Lew Leslie’s

International Revue, it became a standard and has been covered by the greats: Louis Armstrong,

Dizzy Gillespie, Billie Holiday, and Ella Fitzgerald, to name a few. I chose the piece not only

because of the familiarity, but also because the harmonic content is relatively simple and can be

theoretically reduced to its tonic key, which allows for similarities in the way the vocalists could

approach the improvisation.

For the Hindustani section of the study, I chose the raga yaman, a raga that is considered

to be one of the most fundamental ragas. Because the sangram is quite easy and close to the

natural raga, most students of Hindustani music learn it at the beginning of their studies. The

pitch content of the raga yaman is the same as the Western Lydian mode (the same as the major

scale but with the raised fourth). The tala was the teentaal, a very common 16-beat cycle.

As with all cross-cultural studies, I had to balance between controlling for variables

between the subjects’ performance conditions and maintaining the integrity of their process.

Though the usual performance of Hindustani music is much longer, more elaborated and not as

dichotomized, this version still engaged the participants in familiar material that was more

comparable to the jazz performance.

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1.4: Objectives

I had two objectives for this study:

1) To determine the differing patterns of brain activity during composed and

improvised music performances in both Western jazz musicians and Eastern

Hindustani musicians.

2) To determine if there are different patterns of brain activity between Western

jazz and Eastern Hindustani musicians when performing improvised pieces.

1.5: Hypotheses

As mentioned previously, alpha oscillations have repeatedly come up in EEG studies

focusing on both jazz improvisation and improvisational therapy. Strong frontal alpha

synchronizations have been associated with flow state, and seem to be a hallmark of

improvisation. Additionally, improvisation has been associated with a disengagement of sensory

regions. I focused on alpha powers in the frontal and posterior regions. As my experiment is in a

2x2 design, I compared improvisation to composed music within each group, and then compared

improvisation and composed music respectively across groups.

However, as these scientific concepts of improvisation have been derived within the

highly dichotomized improvisation-composed Western setting, they may not be directly

applicable to the style in which Hindustani musicians perform. I believe that the blur between

improvisation and composed music in the Hindustani tradition caused the composed setting to

have improvisatory elements and its according neural substrates.

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Therefore, I hypothesized that there will be greater alpha oscillation synchronization

during improvisation in comparison to the composed performance, especially in the prefrontal

and posterior regions. I believed the distinction will be more evident in the jazz group than the

Hindustani. It follows that between the composed elements, the Hindustani group will have

proved to have more alpha oscillations involved than the jazz group.

Because of the lack of harmonic changes in the Hindustani group, I believed that the

Hindustani participants will have had even less sensory activation during improvisation than the

jazz participants during improvisation. Between the composed portions I hypothesized that there

will be higher levels of alpha in the Hindustani participants, as they incorporate more

improvisatory elements in their compositional sections.

If alpha oscillations are prevalent in the prefrontal and posterior regions, we can

reasonably infer that improvisation incurs a state of relaxation. If there is more alpha prevalence

in Hindustani music, it not only implies that their improvisation would be adequate for music

therapy, but perhaps even more appropriate than jazz improvisation.

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2: Methods I conducted my study in two parts, first with jazz vocalists in Montreal, Canada, and

second with Hindustani vocalists in Bangalore, India. Each part dealt with different musical

content, but the experiment design remained relatively the same. Henceforth, the first part of the

study will be referred to as the jazz group, and the second part of the study will be referred to as

the Hindustani group. Each participant went through the study individually.

2.1: Participants

I had four jazz vocalists in the jazz group and four Hindustani vocalists in the Hindustani

group, totaling in eight participants. All eight participants were female. Only one jazz participant

had a history of mental/psychiatric disorders (depression), and none of them had a history of

brain injury nor drug/alcohol abuse. All were in good health and could read and speak in English

well. The jazz vocalists were recruited through a vocal teacher at the University of Montreal and

the Hindustani vocalists were recruited through Dr. Shantala Hegde, clinical psychologist and

researcher at NIMHANS (as well as a Hindustani vocalist herself). All participants were

informed about the study prior to consenting, in line with the requirements of the Institutional

Review Board at Brown. They were each compensated with a handwritten note and a Brown

University pen.

Prior to the study, each participant was asked to sign an informed consent form and fill

out a demographic questionnaire about their practice and performance practices. All forms are

available in Appendix B. Being that the population size is small and their demographics are

varied, the averages and deviations from the mean differ widely between the two groups. A

breakdown of participant demographics can be seen in Table 2. There are some items of interest

between the two populations that are in line with cultural practices around the two genres of

https://drive.google.com/a/brown.edu/folderview?id=0B9q7T6jIrQ_udGkyM29sbnBGZTg&usp=sharinghttps://docs.google.com/a/brown.edu/document/d/1MJic9tzZCo86sXHKfqaZktXxZSJfLgfEWsxSEHSYChA/edit?usp=sharing

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music. The age at which most jazz vocalists start studying the form is in the late teens/early

twenties, while it is common, if not expected for Hindustani vocalists to start around age 4. This

explains why the average age between groups is similar and the average years of experience in

their field is so drastic, with the Hindustani vocalists averaging about 10 more years of

experience than the jazz vocalists. Overall, the Hindustani population practices more frequently

and for longer than the jazz population does, but the jazz population tends to perform more

frequently per month than the Hindustani population. For more information, refer back to section

1.3c for narratives about the participants’ interaction with their musical genre.

Mean Range

Age Jazz 32.25 23-51

Hindustani 35.50 29-48

Years studying voice Jazz 16.25 6-31

Hindustani 26.75 13-40

Years studying improv Jazz 12.25 6-25

Hindustani 15.38 1.5-25

Practice sessions per week Jazz 5.38 4.5-7

Hindustani 6.50 5-7

Hours per practice session Jazz 1.75 1-2.5

Hindustani 2.19 0.5-4

Performances per month Jazz 5.88 2.5-12

Hindustani 1.08 0.85

Table 2: Participant Demographics. This table shows the mean and range for demographics between jazz and Hindustani participants. Though comparable in age, the Hindustani participants have on average been studying for longer and more per week. However, the jazz participants perform more frequently than their Hindustani counterparts.

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2.2: Materials

I recorded audio, video, and EEG of each participant’s performance. I used the Zoom

H4N Portable Recorder for the audio, the Canon Vixia HF G10 for the video, and the Emotiv

Epoc EEG headset for the EEG. I used the iReal Pro app (Technimo LLC) to provide piano,

bass, and minimal drums for the jazz vocalists and I used the iTabla Pro app (Prasad Upsani) to

provide tanpura and tabla for the Hindustani vocalists. All of the data was backed up in a secure

folder on my personal laptop.

The Emotiv system is a research-quality system3 with fourteen channels (AF3, F7, F3,

FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4 within the international 10-20 system) and two

CMS/DRL references at P3 and P4. It has a sampling rate of 128 SPS via single ADC sequential

sampling. It uses a low-pass sinc filter with notches at 50Hz and 60Hz, leaving a frequency

bandwidth of 0.2 - 43Hz. It is a dry contact system, so the electrodes had only to be wetted with

saline solution prior to setting it on the participants’ heads. The headset uses Bluetooth to

connect with the Emotiv software, TestBench™. TestBench™ software provides real-time

display of EEG, contact quality, FFT, gyro, and marker events. I imported the EEG data from

TestBench™ into EEGLab, an EEG analysis toolbox for MATLAB (R2013b; The MathWorks,

Inc.). EEG is the recording of electrical activity along the scalp; it is useful for examining

changes in power of frequencies over time and at particular moments in time. The data was

preprocessed in a number of ways. I ran the data through a high pass and low pass filter at 5 and

40 Hz respectively. I removed the baseline to average the raw data, and ran independent

component analysis to correct for artifacts, and eliminated channels with excessive artifacts or

poor contact quality.

3 The company states that it is research grade; however, this is contested by some studies and is addressed in the Discussion section.

https://emotiv.com/product-specs/Emotiv%20EPOC%20Specifications%202014.pdf

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2.3: Protocol

Both groups underwent identical procedures, save for the musical content.

Prior to the study, I emailed each participant with information about the study and with the

musical piece to prepare. For the jazz section, I sent them the chart and recording of the changes

to which they would sing in the actual study. I asked all of the jazz participants to perform the

tune “On the Sunny Side of the Street” in G major at a tempo of 140 beats per measure. As I was

unsure as to how familiar each participant was with the standard, they were given the sheet

music and backing instrumental ahead of time so they could prepare. The backing instrumentals

were synthesized piano, bass, and drums from the iReal Pro app. The piece was in G major at a

tempo of 140 beats per minute. The recording went through the form (the changes) three times in

total. The chart and the recording can be found in Appendix B. For the Hindustani section, I

asked them to select a chota khyaal in the raga yaman. As this raga was well-known by all

participants, I gave them no preparatory material. For the background music, I used the tanpura

and tabla sounds in iTabla Pro, also in an analogue to the key of G at a tempo of 140bpm. The

participants performed the first part with the drone alone, and then added the tabla themselves

when ready by clicking on the app. I asked participants in both groups to engage with the

material for at least an hour prior to the study, so that all participants could enter the study at a

similar level of familiarity.

Upon entering the study, I gave them a verbal overview of the study and gave them the

informed consent form, which they read and signed. They also filled out the demographic form

as mentioned above. As I set up the recording devices, I gave them some time to warm up and

practice, if they so chose. I then conducted an informal interview with them, asking about their

experience with and relationship to performing, improvisation, and music in general. I then

arranged the Emotiv headset on their head and adjusted the position and/or saline solution of

https://drive.google.com/a/brown.edu/folderview?id=0B9q7T6jIrQ_udGkyM29sbnBGZTg&usp=sharing

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each electrode to optimize impedance levels. Then I started the accompanying music and asked

them to perform while I recorded audio, video, and EEG.

For the jazz section of the study, I asked them to sing the head once all the way through,

then improvise over the form once all the way through, rest for two As, then come back in with

improvisation for the last BA. For the Hindustani section of the study, I asked the participants to

perform the aroha and avaroha (the ascending and descending scales) of the raga yaman in aa

kar (without the swara syllables, instead with an “aah” sound). After that, I asked them to sing a

chota khyaal (a short composed portion) without too much variation. Then I asked them to

engage in batath (improvisation) for about 3 minutes or so. Then, as with the jazz section, I

asked them to rest for about 30 seconds, then proceed with more improvisation until they saw fit.

I allowed them to choose when they moved through the form, as it was more in line with their

practice. The performance in the jazz section was about 3 minutes, while the performance in the

Hindustani section was about 7-9 minutes. In total, including set up and break down, each study

took about 60 minutes.

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3: Results

3.1: Preparation and selection of the data

I analyzed frontal and posterior regions. For the frontal region, I averaged channels AF3,

F4, and AF4. For the posterior region, I averaged P7, O2, and P8. Channel F3 was defunct prior

to the study, and Channel O1 broke between the conduction of the jazz section and the

Hindustani section. Channel AF4 had poor contact quality for one jazz participant and one

Hindustani participant, so those were removed for the analysis of just those participants. One

jazz participant also did not have usable data from P7, so that too was removed from the

analysis. The system lost contact entirely halfway through one Hindustani participant’s trial, and

so that entire trial was excluded from the analysis. Figure 1a and 1b shows the channel locations

and a visual representation of each participant’s channel contact quality.

Figure 1a: Channel map of the Emotiv system in accordance to the International 10-20 system. AF3, F4, AF4, P7, O2, and P8 were the channels used in this study.

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MJ001 MJ002 MJ003 MJ004

BH002 BH003 BH004

Figure 1b: Channel contact quality for each participant. The jazz participants are in the top row, Hindustani participants in the bottom. BH001 is removed because their trial was rendered defunct by the headset.

I examined the alpha oscillatory patterns in the participants, looking at specifically at the

power of the alpha band (7.5-12.5 Hz) in the frontal and posterior regions. I made 16 second

epochs of three conditions of the study: composed, improvised, and listen. In order to make the

participants’ data comparable to one another, a baseline from which the conditions are contrasted

needs to be established. For instance, one participant could have abnormally high alpha in

general, and so their data could pull the rest of the group up unnecessarily. An ideal baseline

would be EEG data gathered while the participants were at rest. Unfortunately, I did not collect

such data, and so I used the listen condition as the baseline for the alpha band instead. From that,

I constructed a spectrogram for each condition (jazz-composed, jazz-improvised, Hindustani-

composed, Hindustani-improvised) in each region and calculated the average alpha over time,

also with the averaged baseline subtracted.

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3.2: Descriptive Analysis

Unfortunately, the sample size of this study was too small to draw any statistical analysis

from the data, so instead the analysis will be purely descriptive. Spectrograms (or time-frequency

plots) of the respective frontal and posterior regions of the subjects in each condition are shown

in Figures 2 and 3 below. This shows the power of frequencies 5-40Hz as they change over time

in each 16s epoch. I am specifically focusing on the alpha band to see if there are any significant

changes that may correspond with events in the music.

3.2a: Spectrograms

Figure 2: Spectrogram of frontal region. Each spectrogram shows the frequency powers for the first 16sec of each condition in the frontal region (channels AF3, AF4, and F4). This data is plotted relative to the baseline made from the listen condition. Black bars mark the boundaries of alpha (7.5-12.5Hz). A is the composed condition in the jazz group, B is the improvised condition in the jazz group, C is the composed condition in the Hindustani group, and B is the improvised condition in the Hindustani group.

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Figure 3: Spectrogram of posterior region. Each spectrogram shows the frequency powers for the first 16sec of each condition in the posterior region (channels P7, O2, and P8). This data is plotted relative to the baseline made from the listen condition. Black bars mark the boundaries of alpha (7.5-12.5Hz). A is the composed condition in the jazz group, B is the improvised condition in the jazz group, C is the composed condition in the Hindustani group, and B is the improvised condition in the Hindustani group.

In the frontal region, the composed portion of the trial showed periodic strengths and

weaknesses in alpha levels for the jazz participants. There were no apparent trends, save for an

initial burst of alpha strength at the 2 sec mark. The Hindustani participants also shared an alpha

prevalence in the composed section, followed by a marked depression in alpha power at the 3.5-

5.5 sec mark. After that, they had relatively consistent alpha strength. For the improvised section,

the jazz participants had relatively even alpha powers throughout, with slightly stronger values

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initially. The Hindustani participants had overall stronger alpha values in the improvised section,

with a depressing at the 8-10 sec mark.

The posterior region showed only slight differences from the frontal region. In the jazz

composed section, there was a stronger initial burst of alpha at 2 sec, with less evenly distributed

alpha throughout. The Hindustani composed section had a less evident but still present

depression at the 3.5-5.5 sec, with a novel burst at 11sec. The jazz improvised section also has

less evenly distributed alpha, with more evident bursts at 10.5-11.5 sec and 14 sec. The

Hindustani improvised section had much more contrast than its frontal counterpart, with more

alpha everywhere save the 8-11 sec mark.

3.2b: Averaged Alpha To more easily compare alpha powers between groups and conditions, I averaged the

alpha power over time. Below are Figures 4 and 5, graphs of alpha averaged compared to the

baseline over time for the frontal and posterior regions respectively.

Figure 4: Frontal Alpha Averages Figure 5: Posterior Alpha Averages

Overall, all of the alpha averages were negative relative to the baseline, which implies

that there was an overall reduction alpha amplitude relative to the baseline. However, in the

frontal region both the jazz and Hindustani groups had higher alpha power in the improvised

section than in the composed. The Hindustani group had much less alpha in the composed

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section than the jazz, but had higher levels of alpha in the improvised section in relation to jazz.

Posteriorly, the Hindustani group still had higher alpha power in the improvised section than the

jazz group, but both were significantly lower than in the frontal region. The alpha powers for the

composed section were essentially the same for both jazz and Hindustani groups. Interestingly,

though the values were different, the jazz and Hindustani participants shared the same

relationship across brain areas; the jazz participants had higher alpha power in the composed

condition while the Hindustani participants had higher alpha power in the improvised condition.

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4: Discussion

4.1: Discussion of the results

This study aims to use alpha power in the frontal and posterior regions as a lens onto

divergent creative thinking (specifically through improvisation) between two cultural groups,

jazz and Hindustani musicians. The data was partitioned into sixteen second epochs for three

conditions: composed, improvised, and listen. The alpha power in listen condition were used as a

baseline to which the composed and improvised sections were compared. Ideally, a proper

baseline would have been one in which the participants were completely at rest; instead, this

baseline was drawn from a condition in which the participants were not actively performing, but

were processing the background music and perhaps thinking about their next musical decision in

the subsequent condition. This means that the baseline is not static, and so this data must be

taken provisionally. Because the results were drawn in relation to this baseline, it is difficult to

determine whether the outcomes are due to the actual conditions themselves or due to the

dynamism of the baseline.

As stated previously, it was difficult to compare conditions exactly to one another across

cultural groups and practices. One large difference between groups in the experimental design

was that while the jazz participants all sang the same composed piece, the Hindustani

participants performed a chota khyaal of their choosing within the raga yaman. That means that

although inferences can be drawn from the changes in alpha power over time for both groups,

only the data from the jazz group can be specifically synchronized to a certain point in the music.

Letting the Hindustani participants choose their composed piece also adds a variable of

complexity; it is harder to determine what parts were harder for the group to engage with overall

when the compositions may have different periods of complexity.

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It is a point of intrigue, too, that the Hindustani group showed more apparent periods of

lower alpha than the jazz group did. Both groups did have an initial onset of higher alpha levels

in the improvised condition. This could imply that the initial moments of improvisation come

naturally, and once the first motive is played out the next choice must be made, which requires

more executive function.

The finding that all of the averaged alpha powers were negative relative to the baseline

was not in line with the hypothesis of significant alpha activation during improvisation. This may

be due to the nature of the baseline; the participants may have had high levels of alpha while

listening to the music and so the data drawn in relation to that would be negative. The low alpha

powers may also be due to the experimental conditions; the participants were performing in

circumstances quite out of the ordinary. Many of the participants appeared to be nervous

(especially under the uncomfortable EEG headset), and so they may not have been relaxed

enough to enter flow state.

However, in comparing the groups and conditions to one another, we come across

observations that support the hypotheses and observations that contradict it. In the frontal region,

both jazz and Hindustani participants had higher alpha powers in the improvisation condition

than in the jazz. This is in line with the hypothesis that improvisation, a divergent thinking task,

will elicit higher levels of alpha than performing previously composed music. However, the

distinction between alpha power in the composed and improvised sections was greater in the

Hindustani group than in jazz, unlike what I had previously hypothesized. Additionally, the

Hindustani participants had much lower alpha power overall in the composed section than the

jazz participants did. Both of these observations undermine the hypothesis that the lack of

separation between improvisation and composed music in Hindustani tradition would lead to

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higher and more comparable powers of alpha in the Hindustani group. However, this may be

explained by the sheer complexity of Hindustani vocal performance over jazz. Though it is true

that the distinction between improvisation and composed music is much more blurred in

Hindustani, the way in which the musician must adhere to the structure of the raga and develop

tonal relationships is much more demanding than the way (most) jazz vocalists develop the head

of a tune. This is evident in the practice of Hindustani music. As mentioned previously, most

Hindustani vocalists start learning their craft at age four, and will practice for multiple hours

every day of the week. They will learn a couple of ragas and work with them for years on end.

Jazz vocalists, on the other hand, tend to start much later and practice much less. They also tend

to value a breadth of musical knowledge over depth by learning as many standards as possible,

so as to be flexible and dynamic in their performances and jams. A transcription of an excerpt

from one of the Hindustani participants can be examined in relation to the chart for the jazz tune

to see the differences in melodic complexity. This can be found in Appendix B.

The alpha power in the composed condition for both groups remained relatively the same

across frontal and posterior regions. Both groups had much lower alpha power during

improvisation in the posterior region. This is a trend that has been found in multiple studies, in

which there were patterns of decrease from anterior to posterior regions while doing divergent

thinking tasks (Jauk et al. 2012, Fink et al. 2009). Although both groups saw lower alpha powers

posteriorly during improvisation, the jazz and Hindustani participants experienced the same

relationship to each other across conditions and brain areas.

Though the overall negative power of alpha across the board is not in line with my

hypothesis, the comparisons between jazz and Hindustani participants during composed and

improvised conditions do in fact support the theory of frontal alpha-prevalence during divergent

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creative processes such as improvisation. Not only did both groups see higher alpha powers in

the improvised section than the composed, but the Hindustani group showed higher alpha power

in improvised than the jazz group did. This corresponds to the participants’ narratives; when

asked about flow, the jazz participants cited particular circumstances under which they

experience flow, while most of the Hindustani participants stated that they felt flow almost every

time they performed. As stated before, the average Hindustani vocalist starts learning their craft

much sooner and practice more regularly than the average jazz vocalist. This easier access to

flow can be connected to Pressing’s (1988) expertise-related model of improvisation; the more

experience one has, the more automated the lower-level processes are, and the more likely one is

able to experience flow.

Earlier on I addressed the role of musical improvisation and health, specifically its role in

music cognition. I posited that higher alpha powers in the Hindustani improvised condition over

jazz may imply that Hindustani improvisation may be more suitable for music therapy, as alpha

indicates relaxation. Though the results did show a higher alpha power in the Hindustani group

than in jazz in the improvised condition, it appears that the source of the alpha difference is from

years of training and experience that the Hindustani participants have, and so though they may be

more relaxed in the improvised condition, the amount of work needed to achieve that state is

unsuitable for a therapeutic condition. However, this does not preclude non-Western forms of

music being used as tools in music therapy. Usually the improvisation in Western therapy is free-

form and not evaluated based on aesthetic; this means that any form of music, Western or no, can

act as a foundation in which patients can play without restrictions.

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4.2: Limitations of this study

It would be remiss to not examine this study critically. Though this study was designed

with the guidance of many qualified people, there were impediments to the design and execution

that should be addressed and changed in subsequent studies.

The largest issue this study faced was the small sample size. Four participants per group

with data from one of the Hindustani participants defunct left seven total trials, a dataset

insufficient for statistical analysis. Not only was the data small in terms of sample size, but in the

number of available channels as well. The Emotiv EPOC EEG headset, while very attractive in

cost and portability, had a limited number of channels and a fragile construction. The headset’s

connection to the computer was unreliable, and two of the electrodes broke during the data

collection. The Emotiv headset is marketed as research grade, but as Duvinage et al. (2013)

found in their study testing the headset’s ability with an P300 ERP trial, the headset is more

suitable for non-quantitative activities, such as videogames.

Another source of imprecision was the subjective event marking. The markers indicating

the onset of each condition were placed post-trial by synchronizing the video and EEG data by

eye. The markers would have been much more precise if they were placed by the primary

investigator in real-time during the trials. Another flaw in the experimental design was the

location of the trials. In total, there were four different locations in which the study was

conducted: one in Montreal and three in Bangalore. The changes in location in Bangalore were to

accommodate the participants’ schedules, and varied from a bedroom to a practice room to an

EEG lab. Each location had differing levels of distractors, and only one location had a Faraday

cage to reduce electrical noise; this led to differing artifacts per trial. To conduct every trial in

one location would have removed many artifacts from the data.

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Aside from location, there were many inconsistencies between groups. The conditions

themselves varied: in the jazz group they were all asked to sing one particular song, while the

Hindustani group was asked to choose a composed piece within one particular raga. This added

an unnecessary variable to the Hindustani group. Additionally, the Hindustani group did not have

a specific time frame for each condition, while the conditions for the jazz group were temporally

dictated by the form of the song; this makes temporal comparisons much more difficult to make.

Though the jazz participants knew more naturally when to switch conditions, they were not

nearly as familiar overall with On the Sunny Side of the Street as the Hindustani participants

were with their chota khyaals within the raga yaman. The raga yaman is one of the simplest and

earliest taught ragas in the Hindustani tradition, and so the participants were very well versed in

it. While some of the jazz participants did know the tune, others were reading the chart as they

sang, which caused many eye movement artifacts in the data. Most of the Hindustani participants

had their eyes closed for the majority of the trial, which has been shown to produce a marked

increase in alpha (Barry et al., 2007). Another difference between the groups was the syllables

used during improvisation; the jazz participants all used their preferred scat syllables, and the

Hindustani participants switched from aa kar to syllables of the swara to even words from the

chota khyaal. Language and music share an intimate relationship, and the utilization of real

words over non-real words with implicit value (scat syllables) over general sounds can influence

which parts of the brain are active during improvisation.

Of course, an issue that must be considered for any type of music cognition study is how

to strike the balance between controlling variables and inhibiting a creative environment. Music

in its natural state is often messy, with many moving parts and events. While studying the neural

substrates of lower-level psychoacoustic or musical processes may be done in isolation, it seems

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that such a cognitively complex function such as improvisation is hard to take out of context.

Limb and Braun (2008), among others, support Burgess et al.’s argument for an “ecologically

valid” model in which the study environment is conducive to accurate reproductions of these

complex functions (2006). Nonetheless if too many variables are involved in the data, it makes it

much more difficult to draw conclusions to specific causes. It is then the responsibility of the

investigator to reconcile the two needs of the study in a way that is both conducive to the creative

process and cogent in its conclusions.

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4.3: Suggestions for future studies

In the context of the limitations to this study described above, below are

recommendations for future studies.

Use a larger sample size. This will allow the general body of data to withstand fallout

and allow for robust statistical analysis.

Use a research grade EEG system with at least 32 channels. Utilizing a more reliable

system with more channels will not only prevent crucial data from being lost, but will also allow

for spatial analysis and functional connectivity analysis. A higher grade system will also be

conducive to adding event markers during the trial.

Conduct all trials in the same space. Though it would mean removing one of their

groups from their home culture, I would recommend conducting each trial in the same location,

preferably a room equipped with a Faraday cage to reduce external electrical noise. That would

help reduce artifacts and eliminate external distractions/variables.

Add a rest section to the conditions. Having some EEG data while the participants are

at rest will provide a much more reliable and stable baseline than my use of the listening

condition provided.

Restrict the Hindustani group to a single chota khyaal. This will allow the investigator

to synchronize commonalities across participants. I would recommend finding a piece that is

comparable in length to On the Sunny Side of the Street (or another jazz standard with simple

harmonic changes). It would also be beneficial to ask the Hindustani participants to improvise

for the same time as they performed the chota khyaal, so as to regulate the timing of each

condition.

Match the participants in experience and familiarity with the piece across groups.

Though it may be difficult, try to find participants with similar years of experience practicing

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their art. That will allow both groups to be compared at the same caliber. Additionally, try to find

participants who are all equally familiar to the piece they are performing. It could be helpful to

give each participant an opportunity to learn (either on their own or with guidance) the piece two

weeks prior, and require a certain amount of practice hours prior to the trial. That way the

participants could engage with the material equally, and not have to divert their attention to a

sheet of music.

Explore additional hypotheses and methods of analysis. As stated above, using a

higher-grade EEG system with more channels would allow for more spatial analysis. It would be

of interest to see exactly where alpha is prevalent, and how that may change over time.

Additionally, looking at the functional connectivity between frontal and sensory regions would

help address the relationship of lower-level sensory input processing to upper-level executive

decision making and how that relates to flow (see 1.1 for more details). Because so much of the

improvisation literature is measured by fMRI, a future study would benefit from using both EEG

and fMRI methods and comparing the results to each